--- title: "aquila vs weaviate-examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-weaviate-weaviate-examples" tools: ["aquila-network-aquila", "weaviate-weaviate-examples"] --- # aquila vs weaviate-examples *GraphCanon updated Aug 23, 2026* ## Verdict Pick aquila if aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches; pick weaviate-examples if weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [weaviate-examples](https://github.com/weaviate/weaviate-examples) has 331 stars, 86 forks, and 12 open issues, last pushed Aug 7, 2025. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [weaviate-examples's repository](https://github.com/weaviate/weaviate-examples). | | [aquila](/tools/aquila-network-aquila.md) | [weaviate-examples](/tools/weaviate-weaviate-examples.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | Weaviate vector database – examples | | Stars | 379 | 331 | | Forks | 26 | 86 | | Open issues | 13 | 12 | | Language | HTML | HTML | | Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [weaviate-examples](/tools/weaviate-weaviate-examples.md) | | --- | --- | --- | | Days since push | 817d | 380d | | Open issues (now) | 13 | 12 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/weaviate-weaviate-examples/trust.md) | ## Decision facts: aquila - **Adopt for:** Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. ## Decision facts: weaviate-examples - **Adopt for:** weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. ## Choose when ### Choose aquila if… - Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors. - When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary - More GitHub stars (379 vs 331) - visibility, not fit. ### Choose weaviate-examples if… - Tags unique to weaviate-examples: deep-learning, examples, vector-database, vector-search. - You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality. - More recently updated (last pushed Aug 7, 2025). ## When NOT to use aquila - If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration - In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ## When NOT to use weaviate-examples - Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. - You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics. ## Common questions ### What is the difference between aquila and weaviate-examples? aquila: Efficient Neural Search Engine. weaviate-examples: Weaviate vector database – examples. See the comparison table for live GitHub stats and shared categories. ### When should I choose aquila over weaviate-examples? Choose aquila over weaviate-examples when Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary; More GitHub stars (379 vs 331) - visibility, not fit. ### When should I choose weaviate-examples over aquila? Choose weaviate-examples over aquila when Tags unique to weaviate-examples: deep-learning, examples, vector-database, vector-search; You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality; More recently updated (last pushed Aug 7, 2025). ### When should I avoid aquila? If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ### When should I avoid weaviate-examples? Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics. ### Is aquila or weaviate-examples more popular on GitHub? aquila has more GitHub stars (379 vs 331). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and weaviate-examples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or weaviate-examples? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [weaviate-examples alternatives](/tools/weaviate-weaviate-examples/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [weaviate-examples markdown twin](/tools/weaviate-weaviate-examples/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/aquila-network-aquila-vs-weaviate-weaviate-examples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aquila or weaviate-examples? aquila: Dormant. weaviate-examples: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for aquila and weaviate-examples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [weaviate-examples trust report](/tools/weaviate-weaviate-examples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aquila-network-aquila`](/api/graphcanon/graph?tool=aquila-network-aquila) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_